Multi-resolution image representation using Markov random fields
Sridhar Lakshmanan, Anil Kumar Jain, Yu Zhong · 2002
This paper presents a new method for representing the spatial information present in digital grey-tone images. The method is based on using multi-resolution decompositions (MRDs) and Markov random fields (MRFs) concurrently. A given image is represented by a MRD of it, along with an optimally estimated set of Gaussian MRF (GMRF) parameters. Since the GMRF parameters are very small in number, this addition to the usual MRD results in only a small increase in the number of bits in the representation. It is shown, however, that such a minor addition helps when reconstructing the (given) original image from its MRD. Experimental results are presented to illustrate the usefulness of this new method.>